Multiepoch Mini-Batch Levenberg–Marquardt Method for Effective Antenna Design Based on Consensus DNN

A multi-epoch mini-batch variant of the Levenberg-Marquardt algorithm (LMA) is proposed for an efficient training of large training sets applied in consensus deep neural network (Consensus DNN) of complex antenna models. In each training epoch the proposed approach divides whole training set into mini-batches and applies multiple iterations on each of them, where initial weights and biases are overtaken from the previously trained mini-batch. By appropriately selecting the mini-batch size and number of iterations per mini-batch, the adapted LMA enables shorter training time and higher accuracy than standard LMA. Owing to this adaptation the training of Consensus DNN is facilitated and its accuracy is improved.